Who’s better at counting wildlife – humans or drones?

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This week’s featured research article has just been published in Methods in Ecology and Evolution by University of Adelaide researchers and examines the accuracy of counting wildlife between humans and drones. Knowing how many individuals are in a wildlife population allows informed management decisions to be made. Ecologists are increasingly using technologies, such as remotely piloted aircraft (RPA – commonly known as “drones,” unmanned aerial systems or unmanned aerial vehicles), for wildlife monitoring applications. Although RPA are widely touted as a cost-effective way to collect high-quality wildlife population data, the validity of these claims is unclear. Using life-sized, replica seabird colonies containing a known number of fake birds, the researchers assessed the accuracy of RPA-facilitated wildlife population monitoring compared to the traditional ground-based counting method (including DEWNR bird nerds). The task for both approaches was to count the number of fake birds in each of 10 replica seabird colonies. The study shows that RPA-derived data are, on average, between 43% and 96% more accurate than the traditional ground-based data collection method. The study also demonstrates that counts from this remotely sensed imagery can be semi-automated with a high degree of accuracy. The researchers state that the ability to collect data with higher accuracy, higher precision and less bias than the existing approach confirms that RPA are a scientifically rigorous data collection tool for wildlife population monitoring. This approach produces a permanent record, providing the unique opportunity to error-check, and even recount with new detection methods, unlike ground count data. The researchers conclude that RPA-facilitated monitoring also presents the opportunity to collect population data without entering breeding grounds or ecologically sensitive areas, thereby avoiding the disturbance associated with ground surveys. Furthermore, as RPA platforms, sensors and computer vision techniques continue to develop, it is likely that the accuracy and cost-effectiveness of RPA-based approaches will also continue to improve. The paper can be downloaded here (or email jennie.fluin@sa.gov.au for a copy).